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Whole Exome Sequencing for
Variant Discovery and Prioritisation
Exomes: Publication Trends
600
Total: 925 (Oct 2012)
500
Papers
400
300
200
100
0
2005
2006
2007
2008
2009
2010
2011
2012
2013
Year
2013: ~ 800 papers
2014: ~ 1200 papers
Forero DA, 2012
NGS Variation Discovery Workflow
(resequencing based)
Variant Discovery Application:
Disease
• An equivalent of the genome would amount
almost 2000 books, containing 1.5 million
letters each (average books with 200 pages)!
• This information is contained in any single cell
of the body.
Monogenic Diseases
• Single mutation
• How do we find it in all those ‘books’?
• A bioinformatics challenge
• NGS sequencers can only read small portions
• So, the library is fragments of pages of the books!
Mendelian Disease Gene Discovery
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Gilissen, Genome Biol 2011
Mendelian Disease Gene Discovery
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Gilissen, Genome Biol 2011
Opportunities and Challenges
• Exomes more cost effective: Sequence patient DNA and
filter common SNPs; compare parents child trios; compare
paired normal cancer
• Challenges:
– Still can’t interpret many Mendelian disorders
– Rare variants need large samples sizes
– Exome might miss region (e.g. novel non-coding genes)
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Shendure, Genome Biol 2011
Why exome sequencing?
• WGS still too costly
• WES: targets ~1% of human genome)
• Mendelian disorders  mostly disrupts protein-coding sequences
• Large fraction of rare non-synonymous variants in human genome
are predicted to be deleterious
• Splice sites also enriched for highly functional variation
• Search for variants with large effect sizes
A representation of the relationship between the size of the
mutational target and the frequency of disease for disorders
caused by de novo mutations
Gilissen, Genom Biol 2011
Maximizing chances of finding disease-causing rare variants
using exome sequencing
Bamshad, Nat Rev Genet 2011
Example: Comparative
Sequencing
• Somatic mutation
detection between
normal / cancer pairs
• More mutation yield
and better causal
gene identification
than Mendelian
disorders
Meyerson et al, Nat Rev Genet 2010
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BUT Exome Analysis for a single
patient can be informative
Perrault syndrome (HSD17B4)
Pierce, Am J Hum Genet 2010
Exome sequencing procedure
Read Mapping
• Mapping hundreds of millions of reads the reference
genome is CPU and RAM intensive, and ‘slow’
• Read quality decreases with length (small single
nucleotide mismatches or indels – real or artifact?)
• Very few mappers appropriately deal with indels
• Mapping output: SAM (BAM) or BED
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Mapped Data: SAM specification
• Simple generic sequence alignment format
• Describes alignment of reads to a reference
• Flexible - stores all the alignment information
• Keeps track of chromosome position, alignment quality and
alignment features (extended cigar)
• Includes mate pair / paired end information
• Original FASTQ data can be reproduced from SAM (and BAM)
SAM FIELDS
BAM format
• Binary version of SAM - more compact
• Makes downstream analysis independent
from the mapping program
• Allows most of operations on alignment to
work on a stream without loading the whole
alignment into memory
• Allows the file to be indexed by genomic
position to efficiently retrieve all reads
aligning to a locus
VCF format
• Emerging standard for storing variant data
• Originally designed for SNPs and short INDELs, it
also works for structural variations
• Consists of header and data sections
• The data section is TAB delimited with each line
consisting of at least 8 mandatory fields
VCF FIELDS
Variant
Prioritization
• Heuristic filtering
to identify novel
genes for
Mendelian
disorders
Stitziel et al, Genome Biol 2011
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More than just SNVs and ‘short’ indels
Example WES-based variant discovery
workflow
1. Map the reads to a reference genome
– index the reference genome
– Map (BWA, BOWTIE, NOVOAOLIGN, ETC)
2.
3.
4.
5.
6.
7.
8.
9.
Sort BAM file
Remove PCR duplicates
Realign around indels (‘optional’)
Call variants
Recalibrate quality scores (‘optional’)
Filter variants
Basic variant annotation
Biological interpretation only starts here